Motivation and Mathematics Performance: A Longitudinal Study in Early Educational Stages

Mercader, Jéssica; Presentación, María-Jesús; Siegenthaler, Rebeca; Molinero, V. Palazuelos; Miranda, Ana

doi:10.1016/j.psicoe.2017.05.008

Cited by 14 publications

(15 citation statements)

References 28 publications

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“…Many studies have also studied predictors of students' academic performance with longitudinal data. For example, Mercader et al (2017) found that children's mastery motivation (persistence) in kindergarten significantly predicted mathematics achievement at the end of primary two. Brock et al (2008) examined how a set of responsive teaching approaches, when applied in the classroom, may impact students' perceptions of their classroom, students' academic and social performance over a span of three years.…”

Section: Current State Of Literaturementioning

confidence: 99%

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Puah¹

2021

Preprint

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Predicting students’ academic performance has long been an important area of research in education. Most existing literature have made use of traditional statistical methods that run into the problems of overfitted models, inability to effectively handle large numbers of participants and predictors, and inability to pick out non-linearities that may be present. Regression-based ML methods that can produce highly interpretable yet accurate models for new predictions, are able to provide some solutions to the aforementioned problems. The present study is the first study that develops and compares between traditional MLR methods and regression-based ML methods (i.e. ridge regression, LASSO regression, elastic net, and regression trees) to predict students’ science performance in the PISA 2015. A total of 198,712 students from 60 countries, and 66 student- and school-related predictors were used to develop the predictive models. Predictive accuracy of the various models built were not that different, however, there were significant differences in the predictors identified as most important by the different methods. Although regression-based ML techniques did not outperform traditional MLR, significant advantages for using ML methods were noted and discussed. Moving forward, we strongly believe that there is merit for using such regression-based ML methods in educational research. Educational research can benefit from adopting ML practices and methods to produce models that can not only be used for explaining factors that influence academic performance prediction, but also for making more accurate predictions on unseen data.

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Section: Current State Of Literaturementioning

confidence: 99%

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Puah¹

2021

Preprint

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“…Motivation for learning may be defined as the initiation of a learning process, the direction set, and the perseverance in path chosen. The relationship between academic motivation and mathematics achievement has been well studied both in elementary (e.g., Mercader et al, 2017) and in high school (e.g., Moenikia and Zahed-Babelan, 2010). Recently, Hammoudi (2019) reported that students more motivated were more willing to find learning opportunities and achieved higher in mathematics than their counterparts.…”

Section: Perceived Competence Perceived Utility Motivation and Emotionsmentioning

confidence: 99%

Predicting Mathematics Achievement in Secondary Education: The Role of Cognitive, Motivational, and Emotional Variables

et al. 2020

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Academic achievement in general, and in mathematics in particular, is positively associated not only with cognitive abilities, but also with emotional and motivational skills. The objective of this study was to analyze the prediction strength of cognitive, motivational, and emotional variables in mathematics achievement throughout high school, considering students' gender and age. A large sample of 2,365 Spanish students from the 4 years of high school (12-16 years old) participated in the study.Students provided information about their intellectual skills, perceived competence in mathematics, perceived utility of mathematics, intrinsic interest in learning, mathematics anxiety, and their causal attributions (for failure and for success), and of their achievement in mathematics. Data showed differences according to gender and the school grade level. The motivational and affective variables did not seem to play an important role in this relationship as predicted in the current study. The results of this study are discussed in light of previous research.

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“…Az elsajátítási motiváció jobb prediktív erővel bír a gyermekek kognitív képességének fejlődésében, mint az intelligencia, döntő szerepet játszik az iskolai teljesítményben (Gilmore, Cuskelly & Purdie, 2003Mercader, Presentación, Siegenthaler, Moliner & Miranda, 2017).…”

Section: Elsajátítási Motivációunclassified

FOCUS teszt: új, számítógép-alapú vizsgálati eszköz 3–8 éves gyermekek számára

Józsa¹,

Barrett²,

Józsa³

et al. 2019

GYNTF

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A tanulmány a FOCUS (Finding Out Children’s Unique Strengths) tesztet ismerteti. A számítógép-alapú új mérőeszköz 3–8 éves kor között vizsgálja az elsajátítási motivációt, a végrehajtó funkciót, valamint a betű- és számismeretet. Ezek fontos komponensei az alapvető tanulási lépcsőknek (ATL). E tényezőknek jelentős szerepe van az iskolai tanulás sikerességében. A tanulmány bemutatja a FOCUS teszt kidolgozásának az elméleti hátterét, a mérőeszköz felépítését, szubtesztjeit. Beszámol az első próbamérés tapasztalatairól. Ennek során 12 óvodás gyermek egyéni verbális vizsgálatával került sor a feladatok kipróbálására. A második próbamérésben már a számítógép-alapú változatot alkalmaztuk, a mintát 3–8 éves gyermekek alkották, összesen 247 fő. Az új mérőeszköz megfelelő reliabilitásúnak és validitásúnak bizonyult. Az online változatot követően elkészült az offline mobil applikáció is. A webalapú mérőeszköz és az applikáció angol és magyar nyelven érhető el. A tanulmány zárása röviden kitér a teszt további fejlesztésére, a tervezett kutatási felhasználásra.

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Motivation and Mathematics Performance: A Longitudinal Study in Early Educational Stages

Cited by 14 publications

References 28 publications

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Predicting Mathematics Achievement in Secondary Education: The Role of Cognitive, Motivational, and Emotional Variables

FOCUS teszt: új, számítógép-alapú vizsgálati eszköz 3–8 éves gyermekek számára

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